# HD-INC-070: US immigration authorities leaned on machine translation for asylum cases, and errors as small as a pronoun reportedly led to rejections

> The Guardian and Rest of World reported that US agencies used machine translation tools, including Google Translate and contracted services, in asylum processing. Translator groups documented cases where errors carried consequences: an Afghan woman's affidavit reportedly had every 'I' rendered as 'we', and the group says her application was rejected; a domestic violence survivor's 'mi jefe', a colloquialism for her father, became 'my boss'. Advocates say applications were denied over the government's own translation errors.

- Source: https://useheadlights.com/library/HD-INC-070/
- Publisher: Headlights (useheadlights.com), author Ellie Harris
- Published: 2026-07-04
- Company: US Department of Homeland Security (deployer); Google Translate, Lionbridge, TransPerfect (tools and contractors, as reported)
- Sector: Immigration
- Country: United States
- Year: 2023
- Behaviour in the record (Headlights reading): Automation bias
- Governance domains: Administrative law, Human oversight
- Licence: CC BY-NC 4.0. Cite as: Harris, E. (2026). US immigration authorities leaned on machine translation for asylum cases, and errors as small as a pronoun reportedly led to rejections. Headlights AI Incident Library, HD-INC-070. https://useheadlights.com/library/HD-INC-070/

## What happened

Through 2023, reporting by The Guardian, Rest of World, and Context documented US immigration authorities relying on machine translation in asylum processing, from Customs and Border Protection's app-based tools to, per The Guardian, instructions for officials to use Google Translate when vetting refugee applications, alongside contracts with translation firms including Lionbridge and TransPerfect. The tools were being applied to the highest-stakes documents in the system: affidavits, interview records, and applications where a single wrong word can read as an inconsistency, and inconsistency can mean refusal.

The documented errors were small and consequential. Respond Crisis Translation, a translator network whose casework anchored the Guardian reporting, described an Afghan woman's asylum affidavit in which a machine rendered every "I" as "we"; the group says her application was then rejected, in its account because the claim appeared to be filed on behalf of more than one person. In another case the group described, a domestic violence survivor referred to her abuser as "mi jefe", a colloquialism for her father; the tool translated it as "my boss", and the group says she was initially denied. Rest of World reported that machine translations of Pashto and Dari were riddled with errors, contributing to at least one rejected Afghan claim. The group says several of these cases were won only after human translators redid the work. The agencies have made no formal admission that translation errors caused wrongful denials, and no regulator has ruled on the practice.

## What an auditable version would have shown

A refusal built on a mistranslation looks, in the file, like a refusal built on the applicant's own words. That is what makes this failure mode so hard to appeal: the error wears the applicant's voice. An auditable version binds every machine-translated passage in a case file to a signed record of what produced it: the tool and version, the source text, the output, and whether any qualified human reviewed it before it entered the decision. When an adjudicator cites an inconsistency, the record shows whether the inconsistency belongs to the applicant or to the software, which is the difference between a credibility finding and a transcription error.

## Where the gap was

Machine output entered consequential legal decisions as if it were verified fact. A VerificationGate routes high-stakes translations to a trusted check before they can support a decision: a qualified human reviewer for the language pair, flagged confidence on low-resource languages, a block on unreviewed output in credibility determinations. A ConductRecord makes the tool's role visible in the file itself, so an appeal can target the actual source of an error instead of arguing against a machine's words attributed to a human.

## What governance should have looked like

The rule for automated language in high-stakes process is the same as for any model output: it is a draft until something trustworthy has verified it, and its provenance must travel with it. A system that lets unreviewed machine translation stand as an applicant's testimony has quietly delegated a life-altering credibility judgment to a tool that was never designed for it, and has left no trace of the delegation. The record should always be able to answer one question: whose words are these?

The reference implementation of VerificationGate and ConductRecord is open source. It lives at
[github.com/saffronandindia/headlights-oss](https://github.com/saffronandindia/headlights-oss),
Apache 2.0 licensed and free to install. The repository is public now.

## Sources

- [Lost in AI translation: growing reliance on language apps jeopardizes some asylum applications (The Guardian)](https://www.theguardian.com/us-news/2023/sep/07/asylum-seekers-ai-translation-apps)
- [AI translation jeopardizes Afghan asylum claims (Rest of World)](https://restofworld.org/2023/ai-translation-errors-afghan-refugees-asylum/)
- [AI's 'insane' translation mistakes endanger US asylum cases (Context, Thomson Reuters Foundation)](https://www.context.news/ai/ais-insane-translation-mistakes-endanger-us-asylum-cases)

## Related

- [HD-INC-042](/library/HD-INC-042)
- [HD-INC-053](/library/HD-INC-053)
- [HD-INC-050](/library/HD-INC-050)